Satterfield Katherine, Rubin Joshua C, Yang Daniel, Friedman Charles P
Department of Learning Health Sciences University of Michigan Medical School Ann Arbor Michigan.
The Gordon and Betty Moore Foundation Palo Alto California.
Learn Health Syst. 2019 Dec 2;4(1):e210204. doi: 10.1002/lrh2.10204. eCollection 2020.
Inaccurate, untimely, and miscommunicated medical diagnoses represent a wicked problem requiring comprehensive and coordinated approaches, such as those demonstrated in the characteristics of learning health systems (LHSs). To appreciate a vision for how LHS methods can optimize processes and outcomes in medical diagnosis (diagnostic excellence), we interviewed 32 individuals with relevant expertise: 18 who have studied diagnostic processes using traditional behavioral science and health services research methods, six focused on machine learning (ML) and artificial intelligence (AI) approaches, and eight multidisciplinary researchers experienced in advocating for and incorporating LHS methods, ie, scalable continuous learning in health care. We report on barriers and facilitators, identified by these subjects, to applying their methods toward optimizing medical diagnosis. We then employ their insights to envision the emergence of a learning ecosystem that leverages the tools of each of the three research groups to advance diagnostic excellence. We found that these communities represent a natural fit forward, in which together, they can better measure diagnostic processes and close the loop of putting insights into practice. Members of the three academic communities will need to network and bring in additional stakeholders before they can design and implement the necessary infrastructure that would support ongoing learning of diagnostic processes at an economy of scale and scope.
不准确、不及时以及沟通有误的医学诊断是一个棘手的问题，需要采用全面且协调的方法来解决，比如学习型卫生系统（LHS）所展现出的那些方法。为了展望LHS方法如何优化医学诊断的流程及结果（卓越诊断），我们采访了32位相关领域的专家：18位运用传统行为科学和卫生服务研究方法研究诊断流程的专家，6位专注于机器学习（ML）和人工智能（AI）方法的专家，以及8位在倡导和融入LHS方法（即医疗保健中的可扩展持续学习）方面经验丰富的多学科研究人员。我们报告了这些受访者所确定的在应用其方法以优化医学诊断方面的障碍和促进因素。然后，我们利用他们的见解来设想一个学习生态系统的出现，该生态系统利用三个研究小组各自的工具来推动卓越诊断。我们发现，这些群体自然地相互契合，通过合作，他们能够更好地衡量诊断流程，并将见解付诸实践形成闭环。在设计和实施必要的基础设施以支持在规模经济和范围经济下对诊断流程进行持续学习之前，这三个学术群体的成员需要建立联系并引入更多利益相关者。
